The DriveABLE Competence Screen as a predictor of on‐road driving in a clinical sample
Bibliographic record
Abstract
BACKGROUND/AIM: There is growing concern regarding the need for screening of older drivers. The objective of this study was to determine whether the DriveABLE Competence Screen, a computerised test, predicts on-road driving outcome in clients referred for a driving assessment. METHODS: This retrospective study evaluated the predictive validity of pre-road testing using the DriveABLE Screen. Fifty-two clients with varying health conditions were consecutively referred to a private practice that conducts comprehensive driving evaluations. Screen results are classified as recommend cessation of driving, indeterminate (requires on-road evaluation), or no evidence of reduced competence. The DriveABLE Road Test classifies subjects as pass, borderline pass, or fail. RESULTS: Sensitivity, specificity, positive and negative predictive values were generated using the Road Test as the criterion outcome. The positive predictive validity of the Screen in identifying those who would fail the Road Test was 97% (n= 32 of 33). The negative predictive validity was 47%. The sensitivity was 76% with a corresponding specificity of 90%. CONCLUSION: The DriveABLE Screen, when used as a case finding tool, is highly predictive of clients who will fail an on-road driving evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".